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Diffusion Models Tutorial

Diffusion Models Tutorial is a public, MIT-licensed Jupyter Notebook repository for learning diffusion-model theory and implementation and exploring image generation with Stable Diffusion.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Diffusion Models Tutorial — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a hands-on learning path that links diffusion-model concepts with implementation examples and image-generation notebooks.

Who it is for

  • Learners seeking an introduction to diffusion-model concepts.
  • Developers who want a guided example of implementing a simple diffusion model from scratch.
  • People exploring Stable Diffusion image-generation workflows through notebooks.

Documented uses

  • Study diffusion theory and the libraries used to implement diffusion models.
  • Follow a notebook guide to code a simple diffusion model from scratch.
  • Try Stable Diffusion XL notebooks for text-to-image, inpainting, super-resolution or depth-to-image.
  • Learn a basic prompt-building approach using subject, style, artist references and details.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. Local notebook setup uses Anaconda or Miniconda and the supplied Conda environment file; the README also provides an NVIDIA GPU environment file.

  • The repository separates theory, from-scratch implementation, pipeline learning and image-generation examples.
  • Colab notebooks cover four tasks: text-to-image, inpainting, super-resolution and depth-to-image.
  • The README warns that library updates can make notebook examples stop working.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the tutorial's local notebook environment

Create the Conda environment specified for the repository's notebooks.

Before you start

  • Anaconda or Miniconda
  • The repository's environment.yml file

Steps

  1. Obtain the repository files, including environment.yml.

  2. Run conda env create -f environment.yml.

Expected outcome

The Conda environment described by the repository's environment.yml is created.

Guide limitations

  • The README warns that notebook libraries are frequently updated and notebooks may stop working.
  • The GPU-specific environment file is intended for NVIDIA GPU acceleration.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
16 October 2026
Original website title
FilippoMB/Diffusion_models_tutorial · Official homepage title · retained in its source language
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